Complex Diseases and Digital Health Can Push Each Other to Innovate
Digital health can speed the identification and approval of agents in oncology, rare diseases, immunology and neurology.
Digital health is an increasingly hot topic for manufacturers, payers, channel companies, providers and patients, and the FDA has now launched a Digital Health Center of Excellence, essentially making the incorporation of digital technologies into development and commercialization plans a necessity for manufacturers.
Electronic medical and health records (EMRs and EHRs) are a necessity for hospitals, practices and payers. Even patients are now utilizing provider-based EHRs to understand medical histories. Before COVID-19, telehealth was becoming an option for routine maintenance visits, but it is now the preferred choice. Even everyday items, like wearable fitness trackers and smartphones, fit under the wide umbrella of digital health. There is also hope that artificial intelligence (AI) can contribute to better diagnosis, treatment decision making and even drug development.
Manufacturers face a number of questions regarding how to use digital health to optimize clinical development and compete commercially. Early outlets for engaging with patients and physicians from a marketing perspective include e-detailing campaigns, pop-up ads and promotional partnerships with search engines. Digital promotion has become increasingly important during the pandemic, and some of this will likely linger as companies search for and maintain more efficient modes of operation. Ultimately, the best mix of digital health methods will depend on the therapeutic focus, phase of development, product life cycle and overall goal of the digital health solution.
How digital health is utilized for drug development in highly complex and technical therapeutic areas, like oncology, inflammatory conditions and rare diseases, will undoubtedly differ from the strategies used in less acute diseases, such as high blood pressure or diabetes, that tend to be self-managed. The latter diseases are comparatively better understood and/or less complex, so digital health mechanisms, including smartphone apps, telehealth and wearable devices, could have a real impact in maintaining compliance and appointments, while also monitoring fluctuations in parameters like sleep patterns, overall physical activity, blood sugar, oxygen saturation and pulse.
Low-Hanging Fruit in Digital Technologies Across Therapeutic Areas
Whether for participation in clinical trials or everyday treatment, using smartphone apps to remind patients to take their medications is an easy way to improve clinical outcomes, particularly for oral medications and self-injectables. While this digital channel would be particularly useful for younger adults, the elderly population is becoming increasingly comfortable with smartphones, thus making apps a viable option for therapeutic areas that skew toward older populations, such as oncology.
PROs AND AT-HOME MONITORING
Patient-reported outcomes (PROs) are of the utmost importance to demonstrate value to payers. Parameters like mobility, pain, distress, ability to eat and drink, daily living requirements, ability to work, psychological well-being, incontinence and constipation are examples of key indicators of quality of life. All of these require self-reporting and, often, a survey instrument. Digital health can aid in real-time data collection for these variables. Additionally, wearables can augment this data collection by monitoring patterns of daily activity. Even devices, such as handheld ultrasounds, when connected to computers, tablets or smartphones can provide preliminary assessments of bleeding or even clotting events. Similarly, at-home echocardiograms through digital technology could evaluate the risk of stroke or ischemic events.
Real-world evidence (RWE) is increasingly becoming important for manufacturers to demonstrate that results from clinical trials translate into commensurate outcomes from average patients. Access to data collection and aggregation through technologies like EHRs is now considered an essential standard for manufacturers to support payer negotiations. Integration of these closed data systems was a limitation in the past, but major software providers are now increasingly capable of aggregating data. Additionally, these EMR software solutions can provide the best clinical outcomes for patients through standardization of treatment algorithms and clinical pathways. The best pathways allow physicians to have some leeway in decision making, particularly for unique cases.
Next-Generation Application of Digital Technologies
Alternative Means for Regulatory Approval
In our current clinical development paradigm, manufacturers and clinicians assess the efficacy of agents in Phase I and II trials using cross-trial comparisons, historical controls and matched patient cohorts. Accelerated or conditional approvals may be granted without a regulatory standard if the condition has a great unmet need; however, the EMA and FDA require a confirmatory Phase III trial to maintain the conditional approval. In the future, AI may be able to develop digital control arms or synthetic matched patients using historical longitudinal patient records. These computationally derived control arms from RWE would allow for faster and more approvals based on Phase II data, reducing enrollment time and cost without any of the ethical concerns associated with using inactive control arms.
These types of trials would become possible due to wider use of RWE and RWD collection mechanisms for routine patient care. The FDA and NICE have already shown receptivity to using these types of data to aid in regulatory approvals through the 21st Century Cures Act and agreements with Flatiron Health. RWE from routine patient care would contribute to the aggregation and generation of lists of digital patients matching the key conditions of the clinical trials, including key patient characteristics, such as biomarkers, diagnostics, performance status, age, extent of disease, metastatic sites, adverse event profiles and prior therapy.
These RWE platforms could also serve as data aggregators to examine efficacy signals from “off-label” use or even from investigator-sponsored trials (ISTs). ISTs are often small, while the activity of off-label regimens tends to be anecdotal at best. Aggregating the ISTs with RWE from off-label use could represent a large pool of patient data, allowing manufacturers to potentially file for approval in follow-on indications. These types of approvals would, again, likely be useful for rare diseases and cancers with high unmet needs.
Identification of New Targets and Rational Patient Recruitment
Oncology, by nature, focuses on a disease of continually mounting mutations, with the overwhelming degree either not yet elucidated or not yet “druggable”. AI and digital analytics are well placed not only to rationally design new compounds that can focus on key mutations and targets currently believed to be unreachable, but also to pull drugs back into the pipeline that previously did not make it out of Phase I or II due to a lack of understanding of the appropriate patients.
Under this paradigm, we could see the emergence of compounds that target previously difficult MoAs, which could be applied across low-prevalence cancers and rare diseases. Additionally, we could see an increasing reliance on basket trials for low-prevalence driver mutations, which require the pooling of patients in order to show sufficient activity.
Manufacturers have many choices as to how to incorporate digital innovation into development plans while considering a therapeutic focus, phase of development, product life cycle and overall goal of the digital health solution. First, manufacturers need to identify their current successes and what they want to achieve with data analytics with respect to their current marketed assets. Then a similar assessment needs to occur as part of an evaluation of the clinical pipeline, followed by a rational consideration of what types of innovation to incorporate as assets move from preclinical to Phase I. Each asset will have different requirements for data generation for regulatory approval, and therefore may require a unique mix of digital applications.
Manufacturers have several opportunities to speed clinical development as researchers push to initiate innovative trials with novel therapies and to match the appropriate patients to these trials. Meanwhile, payers are encouraging the use of big data sets to find efficiencies in treatment decision making. These market forces can drive the use of a broad spectrum of digital technologies to innovate in complex diseases, such as oncology, immunology, neurology and rare diseases.